‘I Don't Know Why I Feel So Bad Being Asian’: A Qualitative Inquiry of Anti‐Asian Racism From a Racial Trauma Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Despite the incorporation of multiculturalism into Canadian federal policies since the 1970s, whiteness continues to dominate societal norms, perpetuating the racialisation of people of colour. Racialised adolescents are particularly vulnerable to the harmful effects of racialisation and racism. Focusing on Asian Canadian youth, this study adopts a racial trauma perspective to explore their experiences growing up in Canada and the impacts of racism. A total of 36 Asian Canadian youth (aged 14–23) participated in a focus group. Data were analysed using reflexive thematic analysis. Participants reported experiences of alienation and being ‘othered’ during their upbringing. Anti‐Asian racism in Canada often appears in subtle, unacknowledged forms, affecting youth from an early age. These experiences erode self‐esteem and identity, leading some to internalise them as normal and inevitable. Some Asian youth suppress these experiences, gaslighting themselves into self‐blame or denying their existence altogether. Others cope by conforming to whiteness, erasing aspects of their Asian identities. This study highlights the ways in which racial trauma manifests among Asian Canadian youth growing up in a society deeply entrenched in a white racial order, as well as its enduring impacts on their well‐being and sense of self.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it